Social Network Analysis

Social network analysis (SNA) is a methodology to map and examine relationships between various units within a network. SNA can also be viewed as a method of social inquiry, suggests Barry Wellman, which focuses on analysis of relationships between individuals as the primary mechanism for understanding network and individual behavior. Stanley Wasserman and Kathleen Faust propose SNA as a distinctive research perspective that focuses on relational concepts. The big shift in perspective offered by SNA is the ability to study a phenomenon or process relationally, that is, examining units and the relationships between units in a process or entity.

For example, historical notions of learning have focused on the individual, suggesting that isolating the abilities of individual learners can lead to improved understanding of how to engender learning. A social network view, conversely, might suggest that learning is better understood by examining the learner in the context of the various relationships and communities the student is engaged with. If we take a student who has difficulties learning math, one approach might be to test the student’s motivation and knowledge and then design a specific program of instruction to teach the student math. An SNA perspective would suggest trying to look at the student’s current activities, knowledge, and relationships and to identify how and where math might be introduced in a natural and relational manner. For example, can math be introduced as part of the student’s customary game playing or interaction with family and friends? SNA should be viewed as an important additional tool to help clarify how individual behavior and learning is affected by relationships to other individuals. This entry first identifies important components and metrics of social networks. It then discusses how SNA is used to understand dynamics of learning environments and the implications of SNA for learning.

Concepts and Variables Within Social Network Analysis

The simplest components of a social network consist of sets of nodes interconnected by sets of ties, and a specific network structure is identified as a configuration of [Page 669]particular sets of relations between nodes, as suggested by David Knoke and Song Yang. Figure 1 is a simple depiction of a social network. Assuming A and B are friends, and B is friends with C, D, and E, Figure 1 depicts that social network. Each of the individuals is a node or actor in an SNA; each node is linked by a tie or relation.

Figure 1 Simple network sociogram

Assume that A, B, C, D, and E each have other friends, and as is often the case, some of them are also friends with each other. Such an extended network is depicted in Figure 2.

Figure 2 Extended network sociogram

Within a given network configuration, measures of centrality (which deal with individuals) or density (which deal with the group) can be generated. Centrality refers to the prominence of a node’s role within the network. A prominent node is one that is the recipient or initiator of a large number of ties within the network. In Figure 2, B would be considered a prominent node in the network because of high involvement in many relations. By contrast, H and I would be considered to have low prominence. Centrality has various measures, including degree and betweenness. Degree centrality measures the extent to which a node is connected to other nodes in the network. The higher a node’s degree centrality, the more prominent it is in the network. Betweenness centrality measures the extent to which a node lies between other nodes in the network. Thus, even nodes with low-degree centrality can act as intermediaries or gatekeepers between different parts of a network. In Figure 2, A and B both have high betweenness centrality, since information would have to go through A and B to disperse to the entire network.

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